
import numpy as np                # For working with arrays and doing mathematical operations
import pandas as pd               # To analyze data, compatible with reading csv, excel, json etc
import seaborn as sns             # For making statistical graphics
import plotly.express as px       # For data visualization and understanding data
import warnings                   # To ignore various waring, due to depracated packages like is_categorical_dtype
import matplotlib.pyplot as plt;  # Creating static, animated, and interactive visualizations
plt.style.use('ggplot')           # The list of styles available in package, choosing ggplot in our case

#Scikit-learn ( sklearn ) used for predictive data analysis, implementing machine learning models and statistical modelling .
from sklearn.preprocessing import MinMaxScaler   
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import KMeans


# Creating plot using subplots with parameters denoting number of rows and coloums :  (2,3) in below example.    
# figsize with two parameter i.e. width and height in inches. 
# plt.show for showing the graph.
figure, axes = plt.subplots(2, 3, figsize=(8, 5))
plt.show()

# If graph ticks are very close, you can use plt.tight_layout() to add padding between subplots.

figure, axes = plt.subplots(2, 3, figsize=(8, 5))
plt.tight_layout()
plt.show()


# A simple way to create an numpy array in python

x = np.array([0, 1, 2, 3, 4, 5])

# A similar approach as above instead manually doing, giving the number in arange method creates same as above.

y = np.arange(6)

x,y

(array([0, 1, 2, 3, 4, 5]), array([0, 1, 2, 3, 4, 5]))


# To perform various mathematical operations, numpy can do this on array on the fly.

mean = np.mean(x)                 # Mean of all the values in array.
std = np.std(x)                   # Standard deviation of all the values in array.

x = x[3:]                         # Slicing an array just like we do with list in python.
y = y[1:2]
z = x + y
concat = np.concatenate((x, y))   # Merging two array.
sort = np.sort(concat)            # Sorting the value of array using numpy sort method.

x,y,z,concat,sort

(array([3, 4, 5]),
 array([1]),
 array([4, 5, 6]),
 array([3, 4, 5, 1]),
 array([1, 3, 4, 5]))


iris = sns.load_dataset("iris")  # Loding preloaded dataset that comes along with seaborn alising as sns.

iris.head(10)                    # head method to show data, with parameter indicating number of the rows to be shown.


# Using scatterplot method of seaborn with parameters mapping to coloums of the given data in data parameter.

sns.scatterplot(x="sepal_length", y="sepal_width", data=iris)

# Displaying the graph

plt.show()


np.random.seed(0)                  # The random numbers predictable, especially used for debugging purposes
a = np.random.randn(1000)          # Creating 1000 random values on the x-axis.
b = 4 * a + np.random.randn(1000)  # Adding some extra noise to create a linear relationship.

# Create a scatter plot using Seaborn

sns.set(style="whitegrid")  # Setting the style type, explore documentation to learn more.
sns.scatterplot(x=a, y=b)   # Creating a plot showing relationship between datapoints along x and y axis.

# Adding labels along x, y axis and adding title using the below methods.

plt.xlabel("X-axis-label")
plt.ylabel("Y-axis-label")
plt.title("Plot using Seaborn and Numpy")

# This will show the relationship between datapoints of a and b  by plotting their values on a graph.

plt.show()


# Reading the spotify data with parameters as filename and encoding type.

# Source Reference: https://www.kaggle.com/datasets/nelgiriyewithana/top-spotify-songs-2023/code

dataframe = pd.read_csv('spotify-2023.csv', encoding='ISO-8859-1')  # ISO 8859-1 is a single-byte encoding


print('Spotify Data Set Size {}'.format(dataframe.shape))  # Python print syntax to get size of our dataframe.

dataframe.head()        # To view the data using head which by default return first 5 rows.

Spotify Data Set Size (953, 24)


# Info method on dataframe created above helps us to get the information inside the dataframe 

dataframe.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 953 entries, 0 to 952
Data columns (total 24 columns):
 #   Column                Non-Null Count  Dtype 
---  ------                --------------  ----- 
 0   track_name            953 non-null    object
 1   artist(s)_name        953 non-null    object
 2   artist_count          953 non-null    int64 
 3   released_year         953 non-null    int64 
 4   released_month        953 non-null    int64 
 5   released_day          953 non-null    int64 
 6   in_spotify_playlists  953 non-null    int64 
 7   in_spotify_charts     953 non-null    int64 
 8   streams               953 non-null    object
 9   in_apple_playlists    953 non-null    int64 
 10  in_apple_charts       953 non-null    int64 
 11  in_deezer_playlists   953 non-null    object
 12  in_deezer_charts      953 non-null    int64 
 13  in_shazam_charts      903 non-null    object
 14  bpm                   953 non-null    int64 
 15  key                   858 non-null    object
 16  mode                  953 non-null    object
 17  danceability_%        953 non-null    int64 
 18  valence_%             953 non-null    int64 
 19  energy_%              953 non-null    int64 
 20  acousticness_%        953 non-null    int64 
 21  instrumentalness_%    953 non-null    int64 
 22  liveness_%            953 non-null    int64 
 23  speechiness_%         953 non-null    int64 
dtypes: int64(17), object(7)
memory usage: 178.8+ KB


# describe method gives us the descriptive statistics of the dataframe 

dataframe.describe()


# Creating list of coloums to drop and passing it as a parameter in drop method on dataframe.

drop_columns = ['liveness_%', 'instrumentalness_%', 'mode', 'key','valence_%']

dataframe = dataframe.drop(columns=drop_columns)

dataframe.head(10)


# If you want to get only speficied coloum like 'artist(s)_name' from the dataframe

dataframe['artist(s)_name']

0        Latto, Jung Kook
1             Myke Towers
2          Olivia Rodrigo
3            Taylor Swift
4               Bad Bunny
              ...        
948          Selena Gomez
949          Taylor Swift
950    Feid, Paulo Londra
951    Feid, Sech, Jhayco
952             Burna Boy
Name: artist(s)_name, Length: 953, dtype: object


# Reading specific rows of the 'artist(s)_name' column

dataframe['artist(s)_name'][0:5]

0    Latto, Jung Kook
1         Myke Towers
2      Olivia Rodrigo
3        Taylor Swift
4           Bad Bunny
Name: artist(s)_name, dtype: object


# To read the specific row, by giving a specific row number into iloc method

dataframe.iloc[0]

track_name              Seven (feat. Latto) (Explicit Ver.)
artist(s)_name                             Latto, Jung Kook
artist_count                                              2
released_year                                          2023
released_month                                            7
released_day                                             14
in_spotify_playlists                                    553
in_spotify_charts                                       147
streams                                           141381703
in_apple_playlists                                       43
in_apple_charts                                         263
in_deezer_playlists                                      45
in_deezer_charts                                         10
in_shazam_charts                                        826
bpm                                                     125
danceability_%                                           80
energy_%                                                 83
acousticness_%                                           31
speechiness_%                                             4
Name: 0, dtype: object


# Similarly if you want to read a specific row and coloum value of cell by specifying rows and coloum in iloc method

dataframe.iloc[2, 1]

'Olivia Rodrigo'


# To sort the series of the dataframe according to specific columns 

dataframe.sort_values(["released_year"], ascending = False)


# To find the unique values of specific column of a series

dataframe["released_year"].unique()

array([2023, 2019, 2022, 2013, 2014, 2018, 2017, 2020, 2016, 2012, 1999,
       2008, 1975, 2021, 2015, 2011, 2004, 1985, 2007, 2002, 2010, 1983,
       1992, 1968, 1984, 2000, 1997, 1995, 2003, 1973, 1930, 1994, 1958,
       1957, 1963, 1959, 1970, 1971, 1952, 1946, 1979, 1950, 1942, 1986,
       2005, 1991, 1996, 1998, 1982, 1987], dtype=int64)


# Converting datatype using astype(datatype) and combining day, month, year into a new coloum into new format 'release_date'
# You can split the code into multiple line using \ character

dataframe['release_date'] = pd.to_datetime(dataframe['released_year'].astype(str) \
                                           + '-' + dataframe['released_month'].astype(str) \
                                           + '-' + dataframe['released_day'].astype(str), format='%Y-%m-%d', errors='coerce')

# Same as a previous example to drop the coloums

drop_columns = ['released_year', 'released_month', 'released_day']
dataframe = dataframe.drop(columns=drop_columns)

dataframe.head()


# To ignore the warnings due to deprecated packages 

warnings.filterwarnings("ignore", "is_categorical_dtype")
warnings.filterwarnings("ignore", "use_inf_as_na")

# Getting the top 10 Singer and making the bar plot graph from the data.

top_singer = dataframe['artist(s)_name'].value_counts().head(20)


plt.figure(figsize=(12, 6))
sns.barplot(x=top_singer.values, y=top_singer.index, palette='crest')
plt.xlabel('Total Song')
plt.ylabel('Singer Name')
plt.title('Top 20 Singer with most number of  songs')
plt.show()

# BarPlot indicates that Taylor Swift has almost 35 songs, after that singer at 2-5 are almost equal number of songs.


# Getting coloum of bpm and danceability 

beat_per_minute = dataframe['bpm']
dance_ability = dataframe['danceability_%']

# Creating a scatter plot using Seaborn

plt.figure(figsize=(8, 6))
sns.scatterplot(x=beat_per_minute, y=dance_ability, color='green', marker='*')

plt.xlabel('Beat per minute ( BPM )')
plt.ylabel('Danceability')
plt.title('Scatter Plot : BPM vs. Danceability')


# Show the plot
plt.grid(True)
plt.show()

# Analysing the plot it seems there is no correlation between bpm and danceability


# Getting top 10 songs based on the value of streams of spotify data.
# Column streams have weird value at index 574, so we need to clean the data before processing it. 

# Taking reference of dataframe to a variable data

data = dataframe
inconsistent_value = 'BPM110KeyAModeMajorDanceability53Valence75Energy69Acousticness7Instrumentalness0Liveness17Speechiness3'
stream_mean_value = pd.to_numeric(data[data['streams'] != inconsistent_value]['streams']).sort_values(ascending=False).mean()
data['streams'] = data['streams'].replace(inconsistent_value,stream_mean_value)

# Changing the datatype of the streams to numeric

data['streams'] = pd.to_numeric(data['streams'])

top_20_songs = data[['track_name', 'artist(s)_name', 'streams']].sort_values(by='streams', ascending=False).head(20)


plt.figure(figsize=(12, 6))
sns.barplot(x=top_20_songs['streams'], y=top_20_songs['track_name'], palette='flare')
plt.xlabel('Streams (in billions)')
plt.ylabel('Song Name')
plt.title('Top 20 Songs with Most Streams on Spotify')
plt.show()

top_20_songs

# Conclusion
# There are 2 song which are above 3+ billions and rest are above 2+ billions.


# To group the datapoints by artist(s)_name and aggregating number of songs and total streams
# Give a dictionary with column name and a parameter like count,sum,min,max etc to do the operations
# reset_index method will reset the index of the dataframe and use the default one instead

dataframe.groupby('artist(s)_name').agg({'track_name': 'count', 'streams': 'sum'}).reset_index()


# What is the most streamed song of 2023?

# Sorting the datapoints by streams in descending order
stream_sort_by = dataframe.sort_values(by="streams", ascending=False)

# Getting top 30 songs from the sorted dataframe
top_30_songs = stream_sort_by.head(30)

# Graph Plotting
figure = px.bar(top_30_songs, x='track_name', y='streams',
              title='Most streamed tracks in 2023',
              color='track_name',
              color_continuous_scale = 'viridis',
              hover_name = 'artist(s)_name')

# Updating the layout by giving label to x and y axis , category order in descending order
figure.update_xaxes(categoryorder='total descending')
figure.update_xaxes(title_text='Song Names')
figure.update_yaxes(title_text='Total Streams')

# This is for adjusting the layout 
figure.update_layout(width=1200, height=800)

plt.tight_layout()
figure.show()

<Figure size 640x480 with 0 Axes>


singer_statistics = data.groupby('artist(s)_name').agg({'track_name': 'count', 'streams': 'sum'}).reset_index()
singer_statistics.columns = ['Singer', 'Total Songs', 'Net Streams']

# Getting the average stream per songs of each singer and dividing it by a billion
singer_statistics['Streams Average per Song'] = (singer_statistics['Net Streams'] / singer_statistics['Total Songs']) / 1e9

# Sort the data by the number of songs in descending order
singer_statistics = singer_statistics.sort_values(by='Net Streams', ascending=False)

# Getting the top 20 artists
top_20_artists = singer_statistics.head(20)

# Creating an plotty bar chart for Net Streams
figure_one = px.bar(top_20_artists, x='Singer', y='Net Streams',
              title='Most Streamed Singer in 2023',
              color='Singer',
              color_continuous_scale='rainbow')

# Creating an plotty bar chart for total songs sung by singer
figure_two = px.bar(top_20_artists, x='Singer', y='Total Songs',
              title='Total Songs by Singer (Top 20)',
              color='Singer',
              color_continuous_scale='ice')

# Create an interactive bar chart for Average Streams per Song
figure_three = px.bar(top_20_artists, x='Singer', y='Streams Average per Song',
              title='Average Streams per Song (Top 20)',
              color='Singer',
              color_continuous_scale='mint')

# Adjusting  the layout
figure_one.update_xaxes(categoryorder='total descending')
figure_one.update_xaxes(title_text='Singer')
figure_one.update_yaxes(title_text='Total Streams')

figure_two.update_xaxes(categoryorder='total descending')
figure_two.update_xaxes(title_text='Singer')
figure_two.update_yaxes(title_text='Total Songs')

figure_three.update_xaxes(categoryorder='total descending')
figure_three.update_xaxes(title_text='Singer')
figure_three.update_yaxes(title_text='Streams Average per Song')


# Show the bar charts
figure_one.show()
figure_two.show()
figure_three.show()


# Below line is used to ignore warning due to deprecated packages

warnings.filterwarnings('ignore')


s_data = pd.read_csv('spotify-2023.csv', encoding='ISO-8859-1')
s_data.head()


# Creating new relevant information based upon the features coloum of individual songs

features_coloum = [  'liveness_%', 'speechiness_%' ,'danceability_%', 'bpm',
                      'instrumentalness_%', 'valence_%', 'energy_%', 'acousticness_%', ]

s_data[features_coloum].head()

# Standardizes features by scaling each feature to a given range ( 0 to 1 in below example )
# Fit to data, then transform it.

mmscaler = MinMaxScaler()
standardize_features = mmscaler.fit_transform(s_data[features_coloum])

# Converting the standardize features again to dataframe to better handle the data
normalized_dataframe = pd.DataFrame(standardize_features, columns=features_coloum)
normalized_dataframe.head()


# Song Recomendation based upon the features 


from sklearn.metrics.pairwise import cosine_similarity

# Now we can compute similarities
computed_matrix = cosine_similarity(normalized_dataframe)

# Transforming the calculated matrix into a DataFrame for easier handling and reading
computed_dataframe = pd.DataFrame(computed_matrix, index=s_data['track_name'], columns=s_data['track_name'])
computed_dataframe.head(10)


# Defining a function which will suggest songs based upon the song input and number of recommedations.

def songs_suggestions(original_song, recommendations=10):
    
    # Look up the song in our dataframe.
    if original_song not in computed_dataframe.index:
        return "We are sorry, no more song suggestion at this moment"
    
    # Getting the given song name similarlity features and sorting them to get highest similarity 
    song_identical_matrix = computed_dataframe[original_song].sort_values(ascending=False)
    
    # Getting the highest similarity feature value of song excepting the song itself
    suggestions = song_identical_matrix.iloc[1:recommendations+1].index.tolist()
    
    return suggestions

# Passing the random song into our functions and getting the suggested song ouptut
song = "WHERE SHE GOES"
suggestion_output = songs_suggestions(song, 10)
suggestion_output

['After Dark',
 'I Know - PR1SVX Edit',
 'Poland',
 'Apocalypse',
 'B.O.T.A. (Baddest Of Them All) - Edit',
 'METAMORPHOSIS',
 'Makeba',
 'Freaks',
 'Rumble',
 'Master of Puppets (Remastered)']

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa
5	5.4	3.9	1.7	0.4	setosa
6	4.6	3.4	1.4	0.3	setosa
7	5.0	3.4	1.5	0.2	setosa
8	4.4	2.9	1.4	0.2	setosa
9	4.9	3.1	1.5	0.1	setosa

	artist_count	released_year	released_month	released_day	in_spotify_playlists	in_spotify_charts	in_apple_playlists	in_apple_charts	in_deezer_charts	bpm	danceability_%	valence_%	energy_%	acousticness_%	instrumentalness_%	liveness_%	speechiness_%
count	953.000000	953.000000	953.000000	953.000000	953.000000	953.000000	953.000000	953.000000	953.000000	953.000000	953.00000	953.000000	953.000000	953.000000	953.000000	953.000000	953.000000
mean	1.556139	2018.238195	6.033578	13.930745	5200.124869	12.009444	67.812172	51.908709	2.666317	122.540399	66.96957	51.431270	64.279119	27.057712	1.581322	18.213012	10.131165
std	0.893044	11.116218	3.566435	9.201949	7897.608990	19.575992	86.441493	50.630241	6.035599	28.057802	14.63061	23.480632	16.550526	25.996077	8.409800	13.711223	9.912888
min	1.000000	1930.000000	1.000000	1.000000	31.000000	0.000000	0.000000	0.000000	0.000000	65.000000	23.00000	4.000000	9.000000	0.000000	0.000000	3.000000	2.000000
25%	1.000000	2020.000000	3.000000	6.000000	875.000000	0.000000	13.000000	7.000000	0.000000	100.000000	57.00000	32.000000	53.000000	6.000000	0.000000	10.000000	4.000000
50%	1.000000	2022.000000	6.000000	13.000000	2224.000000	3.000000	34.000000	38.000000	0.000000	121.000000	69.00000	51.000000	66.000000	18.000000	0.000000	12.000000	6.000000
75%	2.000000	2022.000000	9.000000	22.000000	5542.000000	16.000000	88.000000	87.000000	2.000000	140.000000	78.00000	70.000000	77.000000	43.000000	0.000000	24.000000	11.000000
max	8.000000	2023.000000	12.000000	31.000000	52898.000000	147.000000	672.000000	275.000000	58.000000	206.000000	96.00000	97.000000	97.000000	97.000000	91.000000	97.000000	64.000000

	track_name	artist(s)_name	streams
55	Blinding Lights	The Weeknd	3.703895e+09
179	Shape of You	Ed Sheeran	3.562544e+09
86	Someone You Loved	Lewis Capaldi	2.887242e+09
620	Dance Monkey	Tones and I	2.864792e+09
41	Sunflower - Spider-Man: Into the Spider-Verse	Post Malone, Swae Lee	2.808097e+09
162	One Dance	Drake, WizKid, Kyla	2.713922e+09
84	STAY (with Justin Bieber)	Justin Bieber, The Kid Laroi	2.665344e+09
140	Believer	Imagine Dragons	2.594040e+09
725	Closer	The Chainsmokers, Halsey	2.591224e+09
48	Starboy	The Weeknd, Daft Punk	2.565530e+09
138	Perfect	Ed Sheeran	2.559529e+09
71	Heat Waves	Glass Animals	2.557976e+09
14	As It Was	Harry Styles	2.513188e+09
693	Seï¿½ï¿½o	Shawn Mendes, Camila Cabello	2.484813e+09
324	Say You Won't Let Go	James Arthur	2.420461e+09
128	lovely - Bonus Track	Billie Eilish, Khalid	2.355720e+09
127	Watermelon Sugar	Harry Styles	2.322580e+09
407	Don't Start Now	Dua Lipa	2.303034e+09
621	Lucid Dreams	Juice WRLD	2.288695e+09
73	Sweater Weather	The Neighbourhood	2.282771e+09

	artist(s)_name	track_name	streams
0	(G)I-DLE	2	2.669833e+08
1	21 Savage, Gunna	1	6.068094e+07
2	24kgoldn, Iann Dior	1	1.699402e+09
3	50 Cent	1	1.202723e+09
4	A$AP Rocky, Metro Boomin, Roisee	1	9.418647e+07
...	...	...	...
640	j-hope	1	1.557958e+08
641	j-hope, J. Cole	1	1.165998e+08
642	sped up 8282	1	1.037625e+08
643	sped up nightcore, ARIZONATEARS, Lil Uzi Vert	1	2.070333e+08
644	teto	1	1.391938e+08

	liveness_%	speechiness_%	danceability_%	bpm	instrumentalness_%	valence_%	energy_%	acousticness_%
0	0.053191	0.032258	0.780822	0.425532	0.000000	0.913978	0.840909	0.319588
1	0.074468	0.032258	0.657534	0.191489	0.000000	0.612903	0.738636	0.072165
2	0.297872	0.064516	0.383562	0.517730	0.000000	0.301075	0.500000	0.175258
3	0.085106	0.209677	0.438356	0.744681	0.000000	0.580645	0.715909	0.113402
4	0.085106	0.064516	0.575342	0.560284	0.692308	0.204301	0.806818	0.144330

Zero To One: The Ultimate Guide To Jupyter Notebook

Importing Libraries : Some Libraries you need to install.¶

Demonstrating the usage of Matplotlib, aliasing as plt¶

Basic usage of Numpy¶

Basic usage of Seaborn¶

Numpy for creating datapoints and Seaborn for creating plot¶

Pandas for reading csv dataset from this link ¶

Basic Method of Pandas¶

Data Visualization of the Spotify data¶

Aggregate function use case in Pandas¶

Basic of Plotly Express¶

Song Recommedation Code using Sklearn¶

	track_name	artist(s)_name	artist_count	released_year	released_month	released_day	in_spotify_playlists	in_spotify_charts	streams	in_apple_playlists	...	bpm	key	mode	danceability_%	valence_%	energy_%	acousticness_%	instrumentalness_%	liveness_%	speechiness_%
0	Seven (feat. Latto) (Explicit Ver.)	Latto, Jung Kook	2	2023	7	14	553	147	141381703	43	...	125	B	Major	80	89	83	31	0	8	4
1	LALA	Myke Towers	1	2023	3	23	1474	48	133716286	48	...	92	C#	Major	71	61	74	7	0	10	4
2	vampire	Olivia Rodrigo	1	2023	6	30	1397	113	140003974	94	...	138	F	Major	51	32	53	17	0	31	6
3	Cruel Summer	Taylor Swift	1	2019	8	23	7858	100	800840817	116	...	170	A	Major	55	58	72	11	0	11	15
4	WHERE SHE GOES	Bad Bunny	1	2023	5	18	3133	50	303236322	84	...	144	A	Minor	65	23	80	14	63	11	6

	track_name	artist(s)_name	artist_count	released_year	released_month	released_day	in_spotify_playlists	in_spotify_charts	streams	in_apple_playlists	in_apple_charts	in_deezer_playlists	in_deezer_charts	in_shazam_charts	bpm	danceability_%	energy_%	acousticness_%	speechiness_%
0	Seven (feat. Latto) (Explicit Ver.)	Latto, Jung Kook	2	2023	7	14	553	147	141381703	43	263	45	10	826	125	80	83	31	4
210	Abcdario	Junior H, Eden Muï¿½ï	2	2023	5	13	262	5	89933133	8	60	4	1	109	129	70	43	78	3
351	PERO Tï¿	Karol G, Quevedo	2	2023	2	23	387	11	93438910	11	15	14	3	1	140	86	79	39	29
174	ýýýýýýýýýýýý	YOASOBI	1	2023	4	12	356	16	143573775	35	102	8	1	117	166	57	94	11	9
348	Red Ruby Da Sleeze	Nicki Minaj	1	2023	3	3	1168	0	81419389	45	11	20	0	21	98	70	73	12	26
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
459	A Holly Jolly Christmas - Single Version	Burl Ives	1	1952	1	1	7930	0	395591396	108	120	73	0	0	140	67	36	64	3
466	Let It Snow! Let It Snow! Let It Snow!	Frank Sinatra, B. Swanson Quartet	2	1950	1	1	10585	0	473248298	126	108	406	0	0	143	60	32	88	6
460	The Christmas Song (Merry Christmas To You) - ...	Nat King Cole	1	1946	11	1	11500	0	389771964	140	72	251	0	0	139	36	15	84	4
469	White Christmas	Bing Crosby, John Scott Trotter & His Orchestr...	3	1942	1	1	11940	0	395591396	73	79	123	0	0	96	23	25	91	3
439	Agudo Mï¿½ï¿½gi	Styrx, utku INC, Thezth	3	1930	1	1	323	0	90598517	4	0	14	0	0	130	65	80	22	5

track_name	Seven (feat. Latto) (Explicit Ver.)	LALA	vampire	Cruel Summer	WHERE SHE GOES	Sprinter	Ella Baila Sola	Columbia	fukumean	La Bebe - Remix	...	Privileged Rappers	The Astronaut	BackOutsideBoyz	Broke Boys	The Great War	My Mind & Me	Bigger Than The Whole Sky	A Veces (feat. Feid)	En La De Ella	Alone
track_name
Seven (feat. Latto) (Explicit Ver.)	1.000000	0.975647	0.880344	0.913981	0.755991	0.936269	0.979482	0.910542	0.855850	0.865533	...	0.921663	0.858991	0.796932	0.741030	0.981515	0.805591	0.528567	0.970908	0.974293	0.943181
LALA	0.975647	1.000000	0.848438	0.876134	0.761163	0.916185	0.917155	0.908580	0.876697	0.819718	...	0.924463	0.890907	0.798221	0.775496	0.978372	0.707324	0.380635	0.984703	0.997566	0.970845
vampire	0.880344	0.848438	1.000000	0.947706	0.808084	0.877307	0.905297	0.879835	0.919387	0.880600	...	0.881834	0.918433	0.875415	0.865885	0.873215	0.860803	0.685082	0.804157	0.850583	0.881730
Cruel Summer	0.913981	0.876134	0.947706	1.000000	0.800570	0.910577	0.931923	0.837759	0.855452	0.922431	...	0.913622	0.914379	0.844334	0.835299	0.899534	0.808021	0.604323	0.850565	0.871277	0.871340
WHERE SHE GOES	0.755991	0.761163	0.808084	0.800570	1.000000	0.753580	0.754172	0.806952	0.811049	0.736370	...	0.765912	0.847082	0.707696	0.764178	0.775037	0.717606	0.529490	0.702161	0.759296	0.812195
Sprinter	0.936269	0.916185	0.877307	0.910577	0.753580	1.000000	0.909120	0.871056	0.917768	0.973289	...	0.988539	0.827115	0.935473	0.872006	0.889540	0.820281	0.558631	0.928006	0.933421	0.896686
Ella Baila Sola	0.979482	0.917155	0.905297	0.931923	0.754172	0.909120	1.000000	0.901235	0.824533	0.870439	...	0.878414	0.839490	0.767688	0.711224	0.957101	0.877326	0.665652	0.905283	0.913442	0.901702
Columbia	0.910542	0.908580	0.879835	0.837759	0.806952	0.871056	0.901235	1.000000	0.919015	0.806597	...	0.849602	0.900670	0.775121	0.816559	0.937927	0.872293	0.645065	0.847442	0.908928	0.973997
fukumean	0.855850	0.876697	0.919387	0.855452	0.811049	0.917768	0.824533	0.919015	1.000000	0.881745	...	0.934555	0.910505	0.923804	0.931598	0.845892	0.822830	0.572939	0.837234	0.894846	0.924033
La Bebe - Remix	0.865533	0.819718	0.880600	0.922431	0.736370	0.973289	0.870439	0.806597	0.881745	1.000000	...	0.955889	0.791919	0.944807	0.888221	0.810054	0.836975	0.642219	0.830181	0.838514	0.815200

Zero To One: The Ultimate Guide To Jupyter Notebook

Importing Libraries : Some Libraries you need to install.¶

Demonstrating the usage of Matplotlib, aliasing as plt¶

Basic usage of Numpy¶

Basic usage of Seaborn¶

Numpy for creating datapoints and Seaborn for creating plot¶

Pandas for reading csv dataset from this link¶

Basic Method of Pandas¶

Data Visualization of the Spotify data¶

Aggregate function use case in Pandas¶

Basic of Plotly Express¶

Song Recommedation Code using Sklearn¶

Pandas for reading csv dataset from this link ¶